get_watermarkRead the invisible watermark code embedded in an image. 이미지에 삽입된 비가시성 워터마크 코드를 조회해 JSON 으로 반환합니다. 이미지가 일부 변형되어도 높은 확률로 워터마크를 확인할 수 있습니다. PNG, JPEG 등 일반 이미지 포맷을 지원합니다. [호출당 10포인트]
| Name | Required | Description | Default |
|---|---|---|---|
| image_url | Yes | 다운로드 가능한 https URL (허용 형식: image/png, image/jpeg, image/webp, image/bmp) (최대 50MB) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, so the read-only nature is covered. The description adds meaningful behavioral context: it returns JSON, works even if the image is partially modified, and costs 10 points per call. These are not present in annotations, enriching the agent's understanding without contradiction.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact, front-loading the core action, then adding return type, robustness, format support, and cost. Each sentence serves a purpose, though the format statement overlaps with schema details. It's well-structured but could trim the redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter tool with no output schema, the description provides essential context: read-only, JSON response, robustness, and cost. It lacks details on the exact JSON structure or error behavior (e.g., no watermark), but given the simplicity and annotations, it is largely complete for invoking correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema fully documents the image_url parameter, including allowed MIME types and maximum size (50MB), achieving 100% coverage. The description's mention of 'PNG, JPEG 등 일반 이미지 포맷' is redundant with the schema and adds no new meaning about the parameter itself, so baseline 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the function: 'Read the invisible watermark code embedded in an image' and notes it returns JSON. It uses a specific verb ('Read' / 조회) that distinguishes it from sibling write tools like set_watermark and draw_watermark_image, so an agent can easily tell them apart.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives no explicit guidance on when to use this tool versus alternatives. The name and verb imply a read operation in contrast to siblings, but there is no statement like 'use this to retrieve, not set, watermarks' or any conditions. Usage is implied rather than stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools have clearly distinct purposes; even within families like identi_card1-5 vs identi_card_image1-5, the text-input vs image-input distinction is clear. However, the sheer number of tools and some near-synonyms (e.g., ocr_identi1 vs identi_card_image1) could cause occasional misselection, but descriptions mitigate this.
Naming follows a loose verb-first pattern (check_, crawl_, download_, draw_, etc.) but includes significant deviations: bare nouns (bank_code, location, whois), numbered variants (identi_card1, identi_card_image1), and mixed prefixes (ocr_, identity_, etc.). The inconsistency is noticeable but still readable and predictable within functional clusters.
80 tools is far above the typical 3-15, but the server is a broad API aggregator covering many independent domains (banking, ID verification, media conversion, search, LLM, etc.), so the high count is somewhat justified. Still, the sheer number makes the toolkit feel unwieldy and hard to navigate, placing it at the high end of acceptable.
Within its stated purpose as a general-purpose utility API, the toolset covers a wide array of common task families: identity document verification (text and image), OCR field extraction, media conversion, web/search, domain/IP lookup, and LLM chat. Most operations have both get and act variants (e.g., set/get watermark, parcel_tracking/auto), with few obvious dead ends for typical use cases.